Martini bead form factors for nucleic-acids and their application in the refinement of protein/nucleic-acid complexes against SAXS data

Paissoni, Cristina; Jussupow, Alexander; Camilloni, Carlo

doi:10.1101/498147

Cited by 7 publications

(16 citation statements)

References 48 publications

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“…Metainference ( 36 ), a method based on Bayesian inference, was used to integrate experimental SAXS data into simulations and was coupled with metadynamics (M&M) ( 47 ). The calculation of the SAXS intensities from a CG Martini representation is implemented in the PLUMED-ISDB module ( 72 , 73 ) using the parameters derived by Niebling et al ( 74 ) and the Debye equation. The SAXS data of the different systems were fitted with a 16th-degree polynomial to calculate points used for restraints.…”

Section: Methodsmentioning

confidence: 99%

The dynamics of linear polyubiquitin

et al. 2020

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Polyubiquitin chains are flexible multidomain proteins, whose conformational dynamics enable them to regulate multiple biological pathways. Their dynamic is determined by the linkage between ubiquitins and by the number of ubiquitin units. Characterizing polyubiquitin behavior as a function of their length is hampered because of increasing system size and conformational variability. Here, we introduce a new approach to efficiently integrating small-angle x-ray scattering with simulations allowing us to accurately characterize the dynamics of linear di-, tri-, and tetraubiquitin in the free state as well as of diubiquitin in complex with NEMO, a central regulator in the NF-κB pathway. Our results show that the behavior of the diubiquitin subunits is independent of the presence of additional ubiquitin modules and that the dynamics of polyubiquitins with different lengths follow a simple model. Together with experimental data from multiple biophysical techniques, we then rationalize the 2:1 NEMO:polyubiquitin binding.

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Section: Methodsmentioning

confidence: 99%

The dynamics of linear polyubiquitin

et al. 2020

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“…Finally, SAXS intensities were also explored as a source of experimental information to be used with metainference. Paissoni and coworkers exploited this strategy to investigate the conformational ensemble of K63-linked diubiquitin [ 148 ] and to refine models of nucleic acid-protein complexes [ 149 ]. Similarly, Kooshapur et al used SAXS experimental data in a metainference framework to derive a structural model of a complex between an RNA-binding protein and a microRNA [ 150 ].…”

Section: Learning From Molecular Dynamics Trajectories and Experimmentioning

confidence: 99%

Data-Driven Molecular Dynamics: A Multifaceted Challenge

2020

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The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

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“…Nevertheless, inaccuracies in the force fields and limitations in terms of the accessible timescales often make their capability to reproduce and to predict experimental results limited [4]. The combination of MD simulations with experimental data is thus emerging as a robust asset to characterize the conformational dynamics of relevant biomolecules [5,6,7,8,9] including RNAs [10,11,12,13,14,15]. Here, MD simulations can be seen as a powerful tool that complements experimental data making it possible to add dynamical information to experiments that report ensemble averages.…”

Section: Introductionmentioning

confidence: 99%

“…Here, the synergy between MD and experiment allows faithful structural ensembles at atomistic resolutions to be generated [18,19,12,20,13,21,22,23]. SAXS experiments are particularly valuable in capturing the structural impact of changes in the ionic conditions, that are highly relevant for RNA but poorly described by force fields [4].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we introduce a protocol exploiting pure-solute forward models [13] and enhanced sampling [33] during MD simulations, to favor sampling of an heterogenous ensemble, without explicitly using the experimental data onthe-fly. The ensemble is then reweighted [9] to match experimental data using accurate explicit-solvent forward models [18].…”

Section: Introductionmentioning

confidence: 99%

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Reweighting of molecular simulations with explicit-solvent SAXS restraints elucidates ion-dependent RNA ensembles

Bernetti

Hall

Bussi

2021

Nucleic Acids Research

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Small-angle X-ray scattering (SAXS) experiments are increasingly used to probe RNA structure. A number of forward models that relate measured SAXS intensities and structural features, and that are suitable to model either explicit-solvent effects or solute dynamics, have been proposed in the past years. Here, we introduce an approach that integrates atomistic molecular dynamics simulations and SAXS experiments to reconstruct RNA structural ensembles while simultaneously accounting for both RNA conformational dynamics and explicit-solvent effects. Our protocol exploits SAXS pure-solute forward models and enhanced sampling methods to sample an heterogenous ensemble of structures, with no information towards the experiments provided on-the-fly. The generated structural ensemble is then reweighted through the maximum entropy principle so as to match reference SAXS experimental data at multiple ionic conditions. Importantly, accurate explicit-solvent forward models are used at this reweighting stage. We apply this framework to the GTPase-associated center, a relevant RNA molecule involved in protein translation, in order to elucidate its ion-dependent conformational ensembles. We show that (a) both solvent and dynamics are crucial to reproduce experimental SAXS data and (b) the resulting dynamical ensembles contain an ion-dependent fraction of extended structures.

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Martini bead form factors for nucleic-acids and their application in the refinement of protein/nucleic-acid complexes against SAXS data

Cited by 7 publications

References 48 publications

The dynamics of linear polyubiquitin

The dynamics of linear polyubiquitin

Data-Driven Molecular Dynamics: A Multifaceted Challenge

Reweighting of molecular simulations with explicit-solvent SAXS restraints elucidates ion-dependent RNA ensembles

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